Information processing apparatus and method

ABSTRACT

This invention relates to an information processing device and method that enable generation of an unlearned new pattern. Data x t  corresponding to a predetermined time series pattern is inputted to an input layer ( 11 ) of a recurrent neural network ( 1 ), and a prediction value x* t+1  is acquired from an output layer  13 . A difference between teacher data x t+1  and the prediction value x* t+1  is learned by a back propagation method, and a weighting coefficient of an intermediate layer  12  is set at a predetermined value. After the recurrent neural network is caused to learn plural time series patterns, a parameter having a different value from the value in learning is inputted to parametric bias nodes ( 11 - 2 ), and an unlearned time series pattern corresponding to the parameter is generated from the output layer ( 13 ). This invention can be applied to a robot.

TECHNICAL FIELD

This invention relates to an information processing device and method,and particularly to an information processing device and method thatenables output of an unlearned new pattern.

This application claims priority of Japanese Patent ApplicationNo.2002-135238, filed on May 10, 2002, the entirety of which isincorporated by reference herein.

BACKGROUND ART

Recently, various studies on human and animal brains have been made. Itis known that a neural network can be used as a brain model.

In a neural network, as a predetermined pattern is learned, the learnedpattern can be identified. However, there is a problem that the neuralnetwork cannot generate a new pattern.

DISCLOSURE OF THE INVENTION

In view of the foregoing status of the art, it is an object of thepresent invention to enable generation of an unlearned new pattern.

An information processing device according to the present inventionincludes: input means for inputting a time series pattern; modeldecision means for deciding a model based on a common nonlinear dynamicsystem having one or more feature parameters that can be operated fromoutside with respect to each of plural time series patterns inputted bythe input means; arithmetic means for calculating a value of the featureparameter on the basis of the decided model; and output means forsetting a value that is different from the value calculated by thearithmetic means, as the feature parameter, and performing inverseoperation of the calculation of the value of the feature parameter,thereby outputting a new time series pattern.

The nonlinear dynamic system can be a recurrent neural network with anoperating parameter.

The feature parameter can indicate a dynamic structure of the timeseries pattern in the nonlinear dynamic system.

The output means can output a new time series pattern having a dynamicstructure that is shareable with plural inputted time series patterns.

An information processing method according to the present inventionincludes: an input step of inputting a time series pattern; a modeldecision step of deciding a model based on a common nonlinear dynamicsystem having one or more feature parameters that can be operated fromoutside with respect to each of plural time series patterns inputted bythe processing of the input step; an arithmetic step of calculating avalue of the feature parameter on the basis of the decided model; and anoutput step of setting a value that is different from the valuecalculated by the processing of the arithmetic step, as the featureparameter, and performing inverse operation of the calculation of thevalue of the feature parameter, thereby outputting a new time seriespattern.

A program of a program storage medium according to the present inventionincludes: an input step of inputting a time series pattern; a modeldecision step of deciding a model based on a common nonlinear dynamicsystem having one or more feature parameters that can be operated fromoutside with respect to each of plural time series patterns inputted bythe processing of the input step; an arithmetic step of calculating avalue of the feature parameter on the basis of the decided model; and anoutput step of setting a value that is different from the valuecalculated by the processing of the arithmetic step, as the featureparameter, and performing inverse operation of the calculation of thevalue of the feature parameter, thereby outputting a new time seriespattern.

A program according to the present invention includes: an input step ofinputting a time series pattern; a model decision step of deciding amodel based on a common nonlinear dynamic system having one or morefeature parameters that can be operated from outside with respect toeach of plural time series patterns inputted by the processing of theinput step; an arithmetic step of calculating a value of the featureparameter on the basis of the decided model; and an output step ofsetting a value that is different from the value calculated by theprocessing of the arithmetic step, as the feature parameter, andperforming inverse operation of the calculation of the value of thefeature parameter, thereby outputting a new time series pattern.

In the information processing device and method, the program storagemedium and the program according to the present invention, a new timeseries pattern corresponding to an inputted time series pattern isoutputted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view showing the structure of a recurrent neural network towhich the present invention is applied.

FIG. 2 is a flowchart for explaining learning processing of therecurrent neural network of FIG. 1.

FIG. 3 is a flowchart for explaining coefficient setting processing ofthe recurrent neural network of FIG. 1.

FIG. 4A is a view showing an exemplary time series pattern havingdifferent amplitude and the same cycle.

FIG. 4B is a view showing an exemplary time series pattern havingdifferent amplitude and the same cycle.

FIG. 4C is a view showing an exemplary time series pattern havingdifferent amplitude and the same cycle.

FIG. 5A is a view showing an exemplary time series pattern having adifferent cycle and the same amplitude.

FIG. 5B is a view showing an exemplary time series pattern having adifferent cycle and the same amplitude.

FIG. 5C is a view showing an exemplary time series pattern having adifferent cycle and the same amplitude.

FIG. 6 is a view showing an exemplary learned pattern.

FIG. 7 is a view showing an exemplary learned pattern.

FIG. 8 is a flowchart for explaining time series pattern generationprocessing of the recurrent neural network of FIG. 1.

FIG. 9 is a view showing an exemplary time series pattern to begenerated.

FIG. 10 is a view showing change of error in the case of causing theneural network to learn a first pattern.

FIG. 11 is a view showing a target in the case of causing the neuralnetwork to learn the first pattern.

FIG. 12 is a view showing an output in the case of causing the neuralnetwork to learn the first pattern.

FIG. 13 is a view showing change of parametric bias in the case ofcausing the neural network to learn the first pattern.

FIG. 14 is a view showing change of error in the case of causing theneural network to learn a second pattern.

FIG. 15 is a view showing a target in the case of causing the neuralnetwork to learn the second pattern.

FIG. 16 is a view showing an output in the case of causing the neuralnetwork to learn the second pattern.

FIG. 17 is a view showing change of parametric bias in the case ofcausing the neural network to learn the second pattern.

FIG. 18 is a view showing change of error in the case of causing theneural network to learn a third pattern.

FIG. 19 is a view showing a target in the case of causing the neuralnetwork to learn the third pattern.

FIG. 20 is a view showing an output in the case of causing the neuralnetwork to learn the third pattern.

FIG. 21 is a view showing change of parametric bias in the case ofcausing the neural network to learn the third pattern.

FIG. 22 is a view showing an example of generated pattern.

FIG. 23 is a view showing another example of generated pattern.

FIG. 24 is a view showing an output corresponding to FIG. 12.

FIG. 25 is a view showing an output corresponding to FIG. 16.

FIG. 26 is a view showing an output corresponding to FIG. 20.

FIG. 27 is a view showing the relation between change of the cycle ofthe patterns of FIGS. 22 to 26 and the parametric bias.

FIG. 28 is a view showing the relation between change of the amplitudeof the patterns of FIGS. 22 to 26 and the parametric bias.

FIG. 29 is a view showing an exemplary pattern to be learned.

FIG. 30 is a view showing another exemplary pattern to be learned.

FIG. 31 is a view showing a pattern generated when the patterns shown inFIGS. 29 and 30 are learned.

FIG. 32 is a view showing change of error in the case of causing theneural network to learn a fourth pattern.

FIG. 33 is a view showing a target in the case of causing the neuralnetwork to learn the fourth pattern.

FIG. 34 is a view showing an output in the case of causing the neuralnetwork to learn the fourth pattern.

FIG. 35 is a view showing change of parametric bias in the case ofcausing the neural network to learn the fourth pattern.

FIG. 36 is a view showing change of error in the case of causing theneural network to learn a fifth pattern.

FIG. 37 is a view showing a target in the case of causing the neuralnetwork to learn the fifth pattern.

FIG. 38 is a view showing an output in the case of causing the neuralnetwork to learn the fifth pattern.

FIG. 39 is a view showing change of parametric bias in the case ofcausing the neural network to learn the fifth pattern.

FIG. 40 is a view showing an example of generated pattern.

FIG. 41 is a view showing an example of generated pattern.

FIG. 42 is a view showing an example of generated pattern.

FIG. 43 is a view showing an output corresponding to FIG. 34.

FIG. 44 is a view showing an output corresponding to FIG. 38.

FIG. 45 is a view showing the relation between the cycle of the patternsof FIGS. 40 to 44 and the parametric bias.

FIG. 46 is a view showing the relation between the amplitude of thepatterns of FIGS. 40 to 44 and the parametric bias.

FIG. 47 is a block diagram showing an exemplary structure of a personalcomputer to which the present invention is applied.

BEST MODE FOR CARRYING OUT THE INVENTION

FIG. 1 shows an exemplary structure of a recurrent neural network towhich the present invention is applied. This recurrent neural network(RNN) 1 includes an input layer 11, an intermediate layer (hidden layer)12, and an output layer 13. Each of these input layer 11, intermediatelayer 12 and output layer 13 includes an arbitrary number of neurons.

Data x_(t) related to a time series pattern is inputted to neurons 11-1,which constitute a part of the input layer 11. Specifically, forexample, the data is related to a time series pattern such as a humanphysical movement pattern (for example, locus of movement of the handposition) acquired by image processing based on camera images. P_(t) isa vector and its dimension is arbitrary depending on the time seriespattern. The parameter P_(t) is inputted to parametric bias nodes 11-2,which are neurons constituting a part of the input layer 11. The numberof parametric bias nodes is one or more. It is desired that the numberof parametric bias nodes is sufficiently small with respect to the totalnumber of neuron that constitute the recurrent neural network and decidethe number of weight matrixes, that is, a parameter of model decisionmeans. In this embodiment, the number of parametric bias nodes is aboutone to two where the total number of such neurons is approximately 50.However, the invention of this application is not limited to thisspecific numbers. The parametric bias nodes are adapted for modulating adynamic structure in a nonlinear dynamic system. In this embodiment, theparametric bias nodes are nodes that function to modulate a dynamicstructure held by the recurrent neural network. However, this inventionis not limited to the recurrent neural network. Moreover, data outputtedfrom neurons 13-2, which constitute a part of the output layer 13, isfed back to neurons 11-3, which constitute a part of the input layer 11,as a context C_(t) expressing the internal state of the RNN 1. Thecontext C_(t) is a common term related to the recurrent neural networkand can be described in a reference literature (Elman, J. L. “Findingstructure in time”, Cognitive Science, 14, (1990), pages 179-211) andthe like.

The neurons of the intermediate layer 12 execute weighted additionprocessing to inputted data and processing to sequentially output theprocessed data to the subsequent stage. Specifically, after arithmeticprocessing (arithmetic processing based on a nonlinear function) with apredetermined weighting coefficient is performed to the data x_(t),P_(t), and C_(t), the processed data are outputted to the output layer13. In this embodiment, for example, arithmetic processing based on afunction having a nonlinear output characteristic such as a sigmoidfunction is performed to the input of a predetermined weighted sum ofx_(t), P_(t), and C_(t), and then the processed data is outputted to theoutput layer 13.

Neurons 13-1, which constitute a part of the output layer 13, outputdata x*_(t+1) corresponding to input data.

The RNN 1 also has an arithmetic unit 21 for learning based on backpropagation. An arithmetic section 22 performs processing to set aweighting coefficient for the RNN 1.

The learning processing of the RNN 1 will now be described withreference to flowchart of FIG. 2.

The processing shown in the flowchart of FIG. 2 is executed with respectto each time series pattern to be learned. In other words, virtual RNNscorresponding to the number of time series patterns to be learned areprepared and the processing of FIG. 2 is executed with respect to eachof the virtual RNNs.

After the processing shown in the flowchart of FIG. 2 is executed withrespect to each of the virtual RNNs and a time series pattern is learnedwith respect to each virtual RNN, processing to set a coefficient to theactual RNN 1 is executed. In the following description, however, eachvirtual RNN is described as the actual RNN 1.

First, at step S11, the neurons 11-1 of the input layer 11 of the RNN 1takes in an input x_(t) at a predetermined time t. At step S12, theintermediate layer 12 of the RNN 1 performs arithmetic processingcorresponding to a weighting coefficient to the input x_(t), and aprediction value x*_(t+1) of a time series t+1 in the inputted timeseries pattern is outputted from the neurons 13-1 of the output layer13.

At step S13, the arithmetic unit 21 takes in an input x_(t+1) at thenext time t+1, as teacher data. At step S14, the arithmetic unit 21calculates the difference between the teacher input x_(t+1) taken in bythe processing of step S13 and the prediction value x*_(t+1) calculatedby the processing of step S12.

At step S15, the RNN 1 inputs the difference calculated by theprocessing of step S14 from the neurons 13-1 of the output layer 13 andpropagates it to the intermediate layer 12 and then to the input layer11, thus performing learning processing. The result of calculationdX_(bpt) is thus acquired.

At step S16, the intermediate layer 12 acquires a modified value dXU ofthe internal state based on the following equation (1).

$\begin{matrix}{{dXU}_{t} = {{k_{bp} \cdot {\sum\limits_{t\frac{1}{2}}^{t + \frac{1}{2}}\;{\mathbb{d}X_{bpt}}}} + {k_{nb} \cdot \left( {{XU}_{t + 1} - {XU}_{t} + {XU}_{t - 1} - {XU}_{t}} \right)}}} & (1)\end{matrix}$

Moreover, the intermediate layer 12 modifies the modified value dXU onthe basis of the following equations (2) to (4).dlXU _(t) =ε·dXU _(t)+momentum·dlXU _(t)  (2)XU _(t) =XU _(t) +dlXU _(t)  (3)X _(t)=sigmoid(XU _(t))  (4)

At step S17, the parametric nodes 11-2 execute processing to save thevalue of the internal state.

Next, at step S18, the RNN 1 judges whether to end the learningprocessing or not. If the learning processing is not to be ended, theRNN 1 returns to step S11 and repeats execution of the subsequentprocessing.

If it is judged at step S18 that the learning processing is to be ended,the RNN 1 ends the learning processing.

As the learning processing as described above is performed, one timeseries pattern is learned with respect to a virtual RNN.

After the learning processing as described above is performed for thevirtual RNNs corresponding to the number of learning patterns,processing to set the weighting coefficient acquired from the learningprocessing, for the actual RNN 1, is performed. FIG. 3 shows theprocessing in this case.

At step S22, the arithmetic section 22 calculates a combined value ofthe coefficients acquired as a result of executing the processing shownin the flowchart of FIG. 2 with respect to each virtual RNN. As thiscombined value, for example, an average value can be used. That is, anaverage value of the weighting coefficients of the respective virtualRNNs is calculated here.

Next, at step S22, the arithmetic section 22 executes processing to setthe combined value (average value) calculated by the processing of stepS21, as a weighting coefficient for the neurons of the actual RNN 1.

Thus, the coefficient acquired by learning the plural time seriespatterns is set for each neuron of the intermediate layer 12 of theactual RNN 1.

The weighting coefficient for each neuron of the intermediate layer 12holds information related to a shareable dynamic structure in order togenerate plural teaching time series patterns, and the parametric biasnodes hold necessary information for switching the shareable dynamicstructure to a dynamic structure suitable for generating each teachingtime series pattern. An example of the “shareable dynamic structure”will now be described. For example, as shown in FIGS. 4A to 4C, when atime series pattern A and a time series pattern B having differentamplitude and the same cycle are inputted, the cycle of an output timeseries pattern C is the shareable dynamic structure. On the other hand,as shown in FIGS. 5A to 5C, when a time series pattern A and a timeseries pattern B having different cycles and the same amplitude areinputted, the amplitude of an output time series pattern C is theshareable dynamic structure. However, the invention of this applicationis not limited to these examples.

For example, as first data is inputted and learned, a time seriespattern indicated by a curve L1 having relatively large amplitude islearned, as shown in FIG. 6.

Similarly, as second data is inputted and learned, a time series patternindicated by a curve L2 having relatively small amplitude is learned, asshown in FIG. 7.

When generating a new time series pattern in the RNN 1 after such timeseries patterns are learned, processing as shown in the flowchart ofFIG. 8 is executed.

Specifically, first, at step S31, the parametric bias nodes 11-2 input aparameter that is different from the parameter in learning. At step S32,the intermediate layer 12 performs calculation based on a weightingcoefficient with respect to the parameter inputted to the parametricbias nodes 11-2 by the processing of step S31. Specifically, inverseoperation of the operation for calculating the parameter value inlearning is carried out.

FIG. 9 shows an example in the case a parameter P_(N) is inputted as aparameter P_(t) to the parametric bias nodes 11-2 of the RNN 1 after theRNN 1 is caused to learn the time series patterns shown in FIGS. 6 and7. This parameter P_(N) has a value that is different from a parameterP_(A) outputted to the parametric bias nodes 11-2 in pattern learning ofFIG. 6 and a parameter P_(B) outputted in time series pattern learningshown in FIG. 7. That is, in this case, the value of the parameter P_(N)is an intermediate value between the values of the parameters P_(A) andP_(B).

In this case, the time series pattern outputted from the neurons 13-1 ofthe output layer 13 is a time series pattern indicated by a curve L3 inFIG. 9. The amplitude of this curve L3 is smaller than the amplitude ofthe curve L1 of the time series pattern A shown in FIG. 6 and largerthan the amplitude of the curve L2 of the time series pattern B shown inFIG. 7. In other words, the amplitude of the curve L3 has anintermediate value between the amplitude of the curve L1 and theamplitude of the curve L2. That is, in this example, the curve L3, whichis an intermediate curve between the curve L1 and the curve L2 shown inFIGS. 6 and 7, is linearly interpolated.

Hereinafter, the result of experiments will be described with referenceto FIGS. 10 to 28.

FIGS. 10 to 13 show error (FIG. 10), target (input data) (FIG. 11),output (FIG. 12), and parametric bias (parameter) (FIG. 13) in the casethe RNN 1 is caused to learn a first time series pattern. The verticalaxes in FIGS. 10 to 13 represent respective values (normalized values),and the horizontal axes represent steps.

When learning the first pattern, the error is almost 0.0 as indicated bya line L11 in FIG. 10. The target (input pattern) is a sine wave asindicated by a line L12 in FIG. 11.

The output corresponding to the target shown in FIG. 11 is a curve (sinewave) substantially corresponding to the curve L12 as a target, asindicated by a curve L13 in FIG. 12.

One of the two parametric bias (parameter) values convergesapproximately at a value of 0.37 as indicated by a curve L14 in FIG. 13,and the other is constant substantially at 0.0 as indicated by a curveL15.

FIGS. 14 to 17 show error (FIG. 14), target (FIG. 15), output (FIG. 16),and parametric bias (parameter) (FIG. 17) in the case the RNN 1 iscaused to learn a second time series pattern.

The error is constant substantially at 0.0 as indicated by a line L21 inFIG. 14. The target is a substantially sine wave-like time seriespattern as indicated by a curve L22 in FIG. 15. In FIG. 15, as is clearfrom the comparison with FIG. 11, the amplitude of the curve L22 issubstantially the same as the amplitude of the curve L12, but the cycleof the curve L22 is longer than the cycle of the curve L12 (that is, thefrequency of the curve L22 is lower).

In accordance with the target shown in FIG. 15, the output of the curve23 substantially corresponding to the curve L22 is acquired, as shown inFIG. 16.

In the case the second time series pattern is learned, one of the twoparametric bias values converges substantially at a value of 0.36 asindicated by a curve L24 in FIG. 17, and the other value convergessubstantially at a value of 0.67 as indicated by a curve L25.

FIGS. 18 to 21 show error (FIG. 18), target (FIG. 19), output (FIG. 20),and parametric bias (FIG. 21) in the case the RNN 1 is caused to learn athird time series pattern.

The error in learning is substantially 0.0 as indicated by a curve L31in FIG. 18. The target is a substantially sine wave-like signal asindicated by a curve L32. As is clear from the comparison with the curveL12 in FIG. 11 and the curve L22 in FIG. 15, the amplitude of the curveL32 is substantially the same as the amplitude of the curve L12 and thecurve L22, but its cycle is longer than that of the curve L22 (that is,the frequency of the curve 32 is lower than that of the curve L22).

As shown in FIG. 20, a curve L33 of output is a sine wave-like curvesubstantially corresponding to the curve L32 of the target shown in FIG.19.

In the case the third time series pattern is learned, one of theparametric bias values is substantially 0.21 as indicated by a curve L34in FIG. 21, and the other value is constant substantially at 1.00 asindicated by a curve L35.

As the RNN 1 was caused to learn three time series patterns as shown inFIGS. 11, 15 and 19 and two values of 0.36 and 0.36 were inputted to theparametric bias nodes 11-2 of the RNN 1 as parametric bias (parameters),a time series pattern indicated by a curve L41 in FIG. 22 was outputtedfrom the neurons 13-1 constituting the output layer 13.

Similarly, when values of 0.80 and 0.25 were inputted as parameters, apattern as indicated by a curve L51 in FIG. 23 was generated. Forcomparison, FIGS. 24 to 26 show time series patterns corresponding tothe targets shown in FIGS. 11, 15 and 19.

As is clear from the comparison of these, the amplitude of the curve L41of FIG. 22 is substantially the same as the amplitude of the curves L13,L23 and L33 of FIGS. 24 to 26. However, the cycle of the curve L41 islonger than the cycle of the curve L13 of FIG. 24 and shorter than thecycle of the curve L23 of FIG. 25.

This is because the parameters (0.36, 0.36) of FIG. 24 are intermediatevalues between the parameters (0.00, 0.37) of FIG. 24 and the parameters(0.67, 0.36) of FIG. 25.

The amplitude of the curve L51 of FIG. 23 is substantially the same asthe amplitude of the curves L13, L23 and L33 of FIGS. 24 to 26 but itscycle is longer than that of the curve L23 of FIG. 25 and shorter thanthe curve L33 of FIG. 26.

This is because the values of the parameters (0.80, 0.25) of FIG. 23 areintermediate values between the parameters (0.67, 0.36) of FIG. 25 andthe parameters (1.00, 0.01) of FIG. 26.

FIG. 27 is a graph showing a cycle where one of the parametric biasvalues is plotted on the horizontal axis and the other value is plottedon the vertical axis. Points A1, A2, A3, B1 and B2 in FIG. 27 representthe plotted parameter values corresponding to FIG. 24 (A1), FIG. 25(A2), FIG. 26 (A3), FIG. 22 (B1) and FIG. 22 (B2). In FIG. 27, the samedensity indicates the same cycle. The gradation of color means smoothchange of the cycle corresponding to the parametric bias values.

FIG. 28 shows change of the amplitude corresponding to two parametricbias values. As is clear from FIG. 28, the amplitude changessubstantially smoothly in accordance with the parametric bias values.

In the above-described example, the RNN 1 is caused to learn linear timeseries patterns. However, similar results were obtained when the RNN 1was caused to learn nonlinear time series patterns.

Specifically, after the RNN 1 is caused to learn a time series patternas indicated by a curve L61 in FIG. 29 and a time series pattern asindicated by a curve L62 in FIG. 30, a parameter P_(M) that is differentfrom a parameter P_(C) acquired when learning the curve L61 of FIG. 29and a parameter P_(D) acquired when learning the pattern of the curveL62 of FIG. 30 is inputted as a parameter to the parametric bias nodes11-2 of the RNN 1. Then, a new pattern that is different from the curveL61 of FIG. 29 and the curve L62 of FIG. 30 can be generated, asindicated by a curve L63.

Specific examples will now be described.

FIGS. 32 to 35 show error (FIG. 32), target (FIG. 33), output (FIG. 34),and parametric bias (FIG. 35) in the case the RNN 1 is caused to learn afourth pattern.

When learning the fourth time series pattern, the error is almost 0.0 asindicated by a curve L71. The target is a sine wave as indicated by acurve L72. The corresponding output is indicated by a curve L73. Thiscurve L73 substantially corresponds to the curve L72.

One of the two parametric bias values acquired in this case is 1.00 asindicated by a curve L74, and the other value is 0.79 as indicated by acurve L75.

FIGS. 36 to 39 show error (FIG. 36), target (FIG. 37), output (FIG. 38),and parametric bias (FIG. 39) in the case the RNN 1 is caused to learn afifth pattern.

The error in this learning is substantially 0.0 as indicated by a curve81 in FIG. 36.

The target is a deformed (strained) sine wave, which is different fromthe smooth sine wave indicated by the curve L72 in FIG. 33, as indicatedby a curve L82 in FIG. 37.

As shown in FIG. 38, an output indicated by a curve L83 corresponding tothe target shown in FIG. 37 is acquired.

In this learning, one of the parametric bias values converges at 0.12 asindicated by a curve L84, and the other value converges substantially at0.60 as indicated by a curve L85.

When parametric bias values (0.12, 0.68) are inputted to the parametricbias nodes 11-2 after the RNN 1 is caused to learn the two time seriespatterns shown in FIGS. 33 and 37, an output indicated by a curve L91 inFIG. 40 is provided.

Similarly, when parametric bias values (0.25, 0.59) are supplied, anoutput indicated by a curve L92 in FIG. 41 is provided. When parametricbias values (0.50, 0.60) are supplied, an output indicated by a curveL93 in FIG. 42 is provided.

To compare the outputs of FIGS. 40 to 42, FIGS. 43 and 44 show outputsobtained in the case parametric bias values (1.00, 0.79) and parametricbias values (0.12, 0.60) are supplied. That is, the output of FIG. 43 isthe same as the output of FIG. 34. The output of FIG. 44 is the same asthe output of FIG. 38.

In this manner, in this example, nonlinear arithmetic processing isperformed to the parameters.

FIG. 45 shows change of the cycle corresponding to the parametric bias.Similarly, FIG. 46 shows change of the amplitude corresponding to theparametric bias. Also in FIGS. 45 and 46, similarly to the cases ofFIGS. 27 and 28, the same density means the same cycle or amplitude. InFIGS. 45 and 46, A1, A2, B1, B2 and B3 represent plotted parametervalues corresponding to FIG. 43, FIG. 44, FIG. 30, FIG. 41 and FIG. 42.

As is clear from FIGS. 45 and 46, also when nonlinear arithmeticprocessing is performed, the cycle and amplitude changes substantiallysmoothly, corresponding to the parameters.

By thus causing the RNN to linear plural time series patterns and theninputting predetermined parameters, it is possible to generate andoutput a new time series pattern that is different from the learned timeseries patterns, using linear or nonlinear interpolation.

The parametric bias is a parameter for switching the spatio-temporalpattern generated by the RNN 1. The relation between this parameter andthe spatio-temporal pattern is not predetermined but is decided bylearning the taught spatio-temporal pattern.

This invention can be applied to, for example, “mimic movement learning”by a humanoid robot. This enables the robot to not only learn pluralmovement patterns (dancing and so on) taught by a person and reproducethose movements, but also extract common characteristics of the pluraltaught movement patterns and generate a new movement pattern based onthe characteristics.

The above-described series of processing, which can be executed byhardware, can also be executed by software. In this case, for example, apersonal computer 160 as shown in FIG. 47 is used.

In FIG. 47, a CPU (central processing unit) 161 executes variousprocessing in accordance with programs stored in a ROM (read-onlymemory) 162 and programs loaded from a storage unit 168 to a RAM(random-access memory) 163. In the RAM 163, necessary data for the CPU161 to execute various processing are properly stored.

The CPU 161, the ROM 162 and the RAM 163 are interconnected via a bus164. Also an input/output interface 165 is connected to this bus 164.

The input/output interface 165 is connected with an input unit 166including a keyboard, a mouse and the like, an output unit 167 includinga display such as a CRT or LCD and a speaker, a storage unit 168including a hard disk, and a communication unit 169 including a modem, aterminal adaptor and the like. The communication unit 169 performscommunication processing via a network.

The input/output interface 165 is also connected with a drive 170, whennecessary. A magnetic disk 171, an optical disc 172, a magneto-opticaldisc 173 or a semiconductor memory 174 is properly loaded on the drive170, and a computer program read from the medium is installed into thestorage unit 168, when necessary.

In the case of executing a series of processing by software, a programconstituting the software is installed into the personal computer 160from a network or a recording medium.

This recording medium may be not only a package medium such as themagnetic disk 171 (including a floppy disk), the optical disc 172(including CD-ROM (compact disc read-only memory) and DVD (digitalversatile disk)), the magneto-optical disc 173 (including MD(mini-disc)) or the semiconductor memory 174 which is distributed toprovide the program to the user separately from the device and in whichthe program is recorded, but also the ROM 162 or the hard disk includedin the storage unit 168 which is provided to the user in the form ofbeing incorporated in the device and in which the program is recorded,as shown in FIG. 47.

In this specification, the step of describing a program to be recordedto a recording medium includes the processing performed in time seriesin the described order and also includes processing executed in parallelor individually, though not necessarily in time series.

Moreover, in this specification, the system means the whole deviceincluding plural units and devices.

While the invention has been described in accordance with certainpreferred embodiments thereof illustrated in the accompanying drawingsand described in the above description in detail, it should beunderstood by those ordinarily skilled in the art that the invention isnot limited to the embodiments, but various modifications, alternativeconstructions or equivalents can be implemented without departing fromthe scope and spirit of the present invention as set forth and definedby the appended claims.

INDUSTRIAL APPLICABILITY

As is described above, according to the present invention, a time seriespattern can be generated. Moreover, the generated time series patterncan be a new time series pattern.

1. An information processing device for outputting a time series patterncomprising: input means for inputting a time series pattern; modeldecision means for deciding a model based on a common nonlinear dynamicsystem having one or more feature parameters that can be operated fromoutside with respect to each of plural time series patterns inputted bythe input means; arithmetic means for calculating a value of the featureparameter on the basis of the decided model; setting means for setting avalue that is different from the value calculated by the arithmeticmeans, as the feature parameter, and performing inverse operation of thecalculation of the value of the feature parameter; and output means foroutputting a new time series pattern as a function of the inverseoperation of the model decision means such that a recurrent neuralnetwork utilizes the new time series pattern for predictive learning. 2.The information processing device as claimed in claim 1, wherein thenonlinear dynamic system is a recurrent neural network with an operatingparameter.
 3. The information processing device as claimed in claim 1,wherein the feature parameter indicates a dynamic structure of the timeseries pattern in the nonlinear dynamic system.
 4. The informationprocessing device as claimed in claim 3, wherein the output meansoutputs a new time series pattern having a dynamic structure that isshareable with plural inputted time series patterns.
 5. An informationprocessing method of an information processing device for outputting atime series pattern the method comprising: inputting a time seriespattern; deciding a model based on a common nonlinear dynamic systemhaving one or more feature parameters that can be operated from outsidewith respect to each of plural time series patterns inputted by theinput step; calculating a value of the feature parameter on the basis ofthe decided model; setting a value that is different from the valuecalculated by the processing of the arithmetic step, as the featureparameter, and performing inverse operation of the calculation of thevalue of the feature parameter; and outputting a new time series patternas a function of the inverse operation of the model deciding step suchthat a recurrent neural network utilizes the new time series pattern forpredictive learning.
 6. A program storage medium having acomputer-readable program stored therein, the program being executed byan information processing device for outputting a time series pattern,the program comprising: an input step of inputting a time seriespattern; a model decision step of deciding a model based on a commonnonlinear dynamic system having one or more feature parameters that canbe operated from outside with respect to each of plural time seriespatterns inputted by the processing of the input step; an arithmeticstep of calculating a value of the feature parameter on the basis of thedecided model; a setting step of setting a value that is different fromthe value calculated by the processing of the arithmetic step, as thefeature parameter, and performing inverse operation of the calculationof the value of the feature parameter; and an output step of outputtinga new time series pattern as a function of the inverse operation of themodel deciding step such that a recurrent neural network utilizes thenew time series pattern for predictive learning.
 7. A computer programfor controlling an information processing device that outputs a timeseries pattern, the program comprising: an input step of inputting atime series pattern; a model decision step of deciding a model based ona common nonlinear dynamic system having one or more feature parametersthat can be operated from outside with respect to each of plural timeseries patterns inputted by the processing of the input step; anarithmetic step of calculating a value of the feature parameter on thebasis of the decided model; a setting step of setting a value that isdifferent from the value calculated by the processing of the arithmeticstep, as the feature parameter, and performing inverse operation of thecalculation of the value of the feature parameter; and an output step ofoutputting a new time series pattern as a function of the inverseoperation of the model decision step such that a recurrent neuralnetwork utilizes the new time series pattern for predictive learning.